Context-dependent Explainability and Contestability for Trustworthy
Medical Artificial Intelligence: Misclassification Identification of
Morbidity Recognition Models in Preterm Infants

Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants

17 December 2022

N. A. Çiftdemir

Papers citing "Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants"

15 / 15 papers shown

Title
Exemplars and Counterexemplars Explanations for Image Classifiers, Targeting Skin Lesion Labeling C. Metta Riccardo Guidotti Yuan Yin Patrick Gallinari S. Rinzivillo MedIm 51 11 0 18 Jan 2023
Explainable AI for clinical and remote health applications: a survey on tabular and time series data Flavio Di Martino Franca Delmastro AI4TS 38 93 0 14 Sep 2022
Human-Centered Explainable AI (XAI): From Algorithms to User Experiences Q. V. Liao R. Varshney 46 227 0 20 Oct 2021
CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays E. Chen Andy Kim R. Krishnan J. Long A. Ng Pranav Rajpurkar 44 2 0 18 Mar 2021
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction Aniruddh Raghu John Guttag K. Young E. Pomerantsev Adrian Dalca Collin M. Stultz 23 9 0 04 Mar 2021
Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyond Guang Yang Qinghao Ye Jun Xia 106 489 0 03 Feb 2021
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies A. Markus J. Kors P. Rijnbeek 78 459 0 31 Jul 2020
CheXplain: Enabling Physicians to Explore and UnderstandData-Driven, AI-Enabled Medical Imaging Analysis Yao Xie Melody Chen David Kao Ge Gao Xiang Ánthony' Chen 90 128 0 15 Jan 2020
Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations Andreas Holzinger André M. Carrington Heimo Muller LRM XAI ELM 58 307 0 19 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI Alejandro Barredo Arrieta Natalia Díaz Rodríguez Javier Del Ser Adrien Bennetot Siham Tabik ... S. Gil-Lopez Daniel Molina Richard Benjamins Raja Chatila Francisco Herrera XAI 105 6,211 0 22 Oct 2019
What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use S. Tonekaboni Shalmali Joshi M. Mccradden Anna Goldenberg 63 389 0 13 May 2019
Learning Interpretable Anatomical Features Through Deep Generative Models: Application to Cardiac Remodeling C. Biffi Ozan Oktay G. Tarroni Wenjia Bai A. de Marvao ... R. Bedair S. Prasad S. Cook D. O’Regan Daniel Rueckert MedIm 44 67 0 18 Jul 2018
A Survey Of Methods For Explaining Black Box Models Riccardo Guidotti A. Monreale Salvatore Ruggieri Franco Turini D. Pedreschi F. Giannotti XAI 101 3,922 0 06 Feb 2018
CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning Pranav Rajpurkar Jeremy Irvin Kaylie Zhu Brandon Yang Hershel Mehta ... Aarti Bagul C. Langlotz K. Shpanskaya M. Lungren A. Ng LM&MA 73 2,691 0 14 Nov 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 746 16,828 0 16 Feb 2016

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